Singular value decomposition for photon-processing nuclear imaging systems and applications for reconstruction and computing null functions

Physics in Medicine and Biology
Abhinav K JhaMatthew A Kupinski

Abstract

Recent advances in technology are enabling a new class of nuclear imaging systems consisting of detectors that use real-time maximum-likelihood (ML) methods to estimate the interaction position, deposited energy, and other attributes of each photon-interaction event and store these attributes in a list format. This class of systems, which we refer to as photon-processing (PP) nuclear imaging systems, can be described by a fundamentally different mathematical imaging operator that allows processing of the continuous-valued photon attributes on a per-photon basis. Unlike conventional photon-counting (PC) systems that bin the data into images, PP systems do not have any binning-related information loss. Mathematically, while PC systems have an infinite-dimensional null space due to dimensionality considerations, PP systems do not necessarily suffer from this issue. Therefore, PP systems have the potential to provide improved performance in comparison to PC systems. To study these advantages, we propose a framework to perform the singular-value decomposition (SVD) of the PP imaging operator. We use this framework to perform the SVD of operators that describe a general two-dimensional (2D) planar linear shift-invariant (LSIV) PP sys...Continue Reading

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Citations

Jun 10, 2016·Medical Physics·Nicolas Gross-WeegeVolkmar Schulz
Apr 4, 2017·Medical Physics·Yijun DingHarrison H Barrett
Nov 19, 2017·Scientific Reports·Yijun DingHarrison H Barrett
Aug 31, 2019·Physics in Medicine and Biology·L CaucciH H Barrett
Mar 11, 2021·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Eric Clarkson, Meredith Kupinski

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